{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ef73850b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "baa5e87e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3.0\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "08f92657",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Vincent\\AppData\\Local\\Temp/ipykernel_3500/337460670.py:1: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.config.list_physical_devices('GPU')` instead.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.test.is_gpu_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "63c9062b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d09c06ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('dataset/Income1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cdc96e88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Education</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>26.658839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>10.401338</td>\n",
       "      <td>27.306435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>10.842809</td>\n",
       "      <td>22.132410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>11.244147</td>\n",
       "      <td>21.169841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>11.645449</td>\n",
       "      <td>15.192634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>12.086957</td>\n",
       "      <td>26.398951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>12.048829</td>\n",
       "      <td>17.435307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>12.889632</td>\n",
       "      <td>25.507885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>13.290970</td>\n",
       "      <td>36.884595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>13.732441</td>\n",
       "      <td>39.666109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>14.133779</td>\n",
       "      <td>34.396281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>14.635117</td>\n",
       "      <td>41.497994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>14.978589</td>\n",
       "      <td>44.981575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>15.377926</td>\n",
       "      <td>47.039595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>15.779264</td>\n",
       "      <td>48.252578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>16.220736</td>\n",
       "      <td>57.034251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>16.622074</td>\n",
       "      <td>51.490919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>17.023411</td>\n",
       "      <td>51.336621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>17.464883</td>\n",
       "      <td>57.681998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>17.866221</td>\n",
       "      <td>68.553714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>18.267559</td>\n",
       "      <td>64.310925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>18.709030</td>\n",
       "      <td>68.959009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>19.110368</td>\n",
       "      <td>74.614639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>19.511706</td>\n",
       "      <td>71.867195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>19.913043</td>\n",
       "      <td>76.098135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>20.354515</td>\n",
       "      <td>75.775216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>20.755853</td>\n",
       "      <td>72.486055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>21.167191</td>\n",
       "      <td>77.355021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>21.598662</td>\n",
       "      <td>72.118790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>80.260571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  Education     Income\n",
       "0            1  10.000000  26.658839\n",
       "1            2  10.401338  27.306435\n",
       "2            3  10.842809  22.132410\n",
       "3            4  11.244147  21.169841\n",
       "4            5  11.645449  15.192634\n",
       "5            6  12.086957  26.398951\n",
       "6            7  12.048829  17.435307\n",
       "7            8  12.889632  25.507885\n",
       "8            9  13.290970  36.884595\n",
       "9           10  13.732441  39.666109\n",
       "10          11  14.133779  34.396281\n",
       "11          12  14.635117  41.497994\n",
       "12          13  14.978589  44.981575\n",
       "13          14  15.377926  47.039595\n",
       "14          15  15.779264  48.252578\n",
       "15          16  16.220736  57.034251\n",
       "16          17  16.622074  51.490919\n",
       "17          18  17.023411  51.336621\n",
       "18          19  17.464883  57.681998\n",
       "19          20  17.866221  68.553714\n",
       "20          21  18.267559  64.310925\n",
       "21          22  18.709030  68.959009\n",
       "22          23  19.110368  74.614639\n",
       "23          24  19.511706  71.867195\n",
       "24          25  19.913043  76.098135\n",
       "25          26  20.354515  75.775216\n",
       "26          27  20.755853  72.486055\n",
       "27          28  21.167191  77.355021\n",
       "28          29  21.598662  72.118790\n",
       "29          30  22.000000  80.260571"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2342291d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a84c5092",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x23a231aec88>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data.Education, data.Income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "706158d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.Education\n",
    "y = data.Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "845baf2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 顺序模型\n",
    "model = tf.keras.Sequential()\n",
    "# 输入数据维度1\n",
    "model.add(tf.keras.layers.Dense(1, input_shape=(1,)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "adb5d306",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 1)                 2         \n",
      "=================================================================\n",
      "Total params: 2\n",
      "Trainable params: 2\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7aa039b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 编译 优化算法adam 损失函数均方差mse\n",
    "model.compile(optimizer='adam', loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f2a08bb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Keras is training/fitting/evaluating on array-like data. Keras may not be optimized for this format, so if your input data format is supported by TensorFlow I/O (https://github.com/tensorflow/io) we recommend using that to load a Dataset instead.\n",
      "Epoch 1/5000\n",
      "1/1 [==============================] - 0s 996us/step - loss: 6075.3281\n",
      "Epoch 2/5000\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 6072.6479\n",
      "Epoch 3/5000\n",
      "1/1 [==============================] - 0s 521us/step - loss: 6069.9683\n",
      "Epoch 4/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 6067.2896\n",
      "Epoch 5/5000\n",
      "1/1 [==============================] - 0s 996us/step - loss: 6064.6113\n",
      "Epoch 6/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 6061.9331\n",
      "Epoch 7/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6059.2563\n",
      "Epoch 8/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6056.5801\n",
      "Epoch 9/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6053.9048\n",
      "Epoch 10/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 6051.2295\n",
      "Epoch 11/5000\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 6048.5552\n",
      "Epoch 12/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6045.8823\n",
      "Epoch 13/5000\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 6043.2095\n",
      "Epoch 14/5000\n",
      "1/1 [==============================] - 0s 13ms/step - loss: 6040.5376\n",
      "Epoch 15/5000\n",
      "1/1 [==============================] - 0s 857us/step - loss: 6037.8662\n",
      "Epoch 16/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 6035.1953\n",
      "Epoch 17/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 6032.5249\n",
      "Epoch 18/5000\n",
      "1/1 [==============================] - 0s 939us/step - loss: 6029.8560\n",
      "Epoch 19/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 6027.1880\n",
      "Epoch 20/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 6024.5205\n",
      "Epoch 21/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6021.8540\n",
      "Epoch 22/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6019.1880\n",
      "Epoch 23/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6016.5229\n",
      "Epoch 24/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 6013.8584\n",
      "Epoch 25/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 6011.1953\n",
      "Epoch 26/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6008.5317\n",
      "Epoch 27/5000\n",
      "1/1 [==============================] - 0s 585us/step - loss: 6005.8706\n",
      "Epoch 28/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6003.2090\n",
      "Epoch 29/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6000.5488\n",
      "Epoch 30/5000\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 5997.8892\n",
      "Epoch 31/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 5995.2314\n",
      "Epoch 32/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 5992.5728\n",
      "Epoch 33/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 5989.9165\n",
      "Epoch 34/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 5987.2603\n",
      "Epoch 35/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 5984.6045\n",
      "Epoch 36/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 5981.9507\n",
      "Epoch 37/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 5979.2974\n",
      "Epoch 38/5000\n",
      "1/1 [==============================] - 0s 396us/step - loss: 5976.6440\n",
      "Epoch 39/5000\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 5973.9927\n",
      "Epoch 40/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 5971.3418\n",
      "Epoch 41/5000\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 5968.6919\n",
      "Epoch 42/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 5966.0420\n",
      "Epoch 43/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 5963.3936\n",
      "Epoch 44/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 5960.7466\n",
      "Epoch 45/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 5958.1001\n",
      "Epoch 46/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 5955.4541\n",
      "Epoch 47/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 5952.8096\n",
      "Epoch 48/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 5950.1650\n",
      "Epoch 49/5000\n",
      "1/1 [==============================] - 0s 997us/step - loss: 5947.5225\n",
      "Epoch 50/5000\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 5944.8804\n",
      "Epoch 51/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 5942.2388\n",
      "Epoch 52/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 5939.5986\n",
      "Epoch 53/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 5936.9595\n",
      "Epoch 54/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 5934.3203\n",
      "Epoch 55/5000\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 5931.6826\n",
      "Epoch 56/5000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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     "text": [
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    {
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     "text": [
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    {
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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    {
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     "text": [
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     ]
    },
    {
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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     "text": [
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     ]
    },
    {
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3273/5000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 3ms/step - loss: 282.7535\n",
      "Epoch 4826/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 282.5292\n",
      "Epoch 4827/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 282.3049\n",
      "Epoch 4828/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 282.0809\n",
      "Epoch 4829/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 281.8571\n",
      "Epoch 4830/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 281.6335\n",
      "Epoch 4831/5000\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 281.4102\n",
      "Epoch 4832/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 281.1870\n",
      "Epoch 4833/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 280.9640\n",
      "Epoch 4834/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 280.7414\n",
      "Epoch 4835/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 280.5189\n",
      "Epoch 4836/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 280.2966\n",
      "Epoch 4837/5000\n",
      "1/1 [==============================] - 0s 950us/step - loss: 280.0746\n",
      "Epoch 4838/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 279.8527\n",
      "Epoch 4839/5000\n",
      "1/1 [==============================] - 0s 986us/step - loss: 279.6312\n",
      "Epoch 4840/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 279.4098\n",
      "Epoch 4841/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 279.1886\n",
      "Epoch 4842/5000\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 278.9677\n",
      "Epoch 4843/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 278.7470\n",
      "Epoch 4844/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 278.5264\n",
      "Epoch 4845/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 278.3062\n",
      "Epoch 4846/5000\n",
      "1/1 [==============================] - 0s 999us/step - loss: 278.0861\n",
      "Epoch 4847/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 277.8662\n",
      "Epoch 4848/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 277.6466\n",
      "Epoch 4849/5000\n",
      "1/1 [==============================] - 0s 878us/step - loss: 277.4272\n",
      "Epoch 4850/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 277.2081\n",
      "Epoch 4851/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 276.9892\n",
      "Epoch 4852/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 276.7704\n",
      "Epoch 4853/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 276.5518\n",
      "Epoch 4854/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 276.3334\n",
      "Epoch 4855/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 276.1154\n",
      "Epoch 4856/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 275.8975\n",
      "Epoch 4857/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 275.6798\n",
      "Epoch 4858/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 275.4623\n",
      "Epoch 4859/5000\n",
      "1/1 [==============================] - 0s 997us/step - loss: 275.2452\n",
      "Epoch 4860/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 275.0281\n",
      "Epoch 4861/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 274.8113\n",
      "Epoch 4862/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 274.5948\n",
      "Epoch 4863/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 274.3784\n",
      "Epoch 4864/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 274.1623\n",
      "Epoch 4865/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 273.9464\n",
      "Epoch 4866/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 273.7307\n",
      "Epoch 4867/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 273.5152\n",
      "Epoch 4868/5000\n",
      "1/1 [==============================] - 0s 997us/step - loss: 273.3000\n",
      "Epoch 4869/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 273.0849\n",
      "Epoch 4870/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 272.8701\n",
      "Epoch 4871/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 272.6554\n",
      "Epoch 4872/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 272.4411\n",
      "Epoch 4873/5000\n",
      "1/1 [==============================] - 0s 683us/step - loss: 272.2269\n",
      "Epoch 4874/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 272.0129\n",
      "Epoch 4875/5000\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 271.7992\n",
      "Epoch 4876/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 271.5856\n",
      "Epoch 4877/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 271.3723\n",
      "Epoch 4878/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 271.1592\n",
      "Epoch 4879/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 270.9464\n",
      "Epoch 4880/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 270.7336\n",
      "Epoch 4881/5000\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 270.5212\n",
      "Epoch 4882/5000\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 270.3090\n",
      "Epoch 4883/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 270.0969\n",
      "Epoch 4884/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 269.8851\n",
      "Epoch 4885/5000\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 269.6735\n",
      "Epoch 4886/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 269.4621\n",
      "Epoch 4887/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 269.2509\n",
      "Epoch 4888/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 269.0399\n",
      "Epoch 4889/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 268.8292\n",
      "Epoch 4890/5000\n",
      "1/1 [==============================] - 0s 867us/step - loss: 268.6187\n",
      "Epoch 4891/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 268.4083\n",
      "Epoch 4892/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 268.1982\n",
      "Epoch 4893/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 267.9884\n",
      "Epoch 4894/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 267.7787\n",
      "Epoch 4895/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 267.5692\n",
      "Epoch 4896/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 267.3600\n",
      "Epoch 4897/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 267.1509\n",
      "Epoch 4898/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 266.9421\n",
      "Epoch 4899/5000\n",
      "1/1 [==============================] - 0s 745us/step - loss: 266.7335\n",
      "Epoch 4900/5000\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 266.5251\n",
      "Epoch 4901/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 266.3170\n",
      "Epoch 4902/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 266.1090\n",
      "Epoch 4903/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 265.9012\n",
      "Epoch 4904/5000\n",
      "1/1 [==============================] - 0s 705us/step - loss: 265.6937\n",
      "Epoch 4905/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 265.4864\n",
      "Epoch 4906/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 265.2792\n",
      "Epoch 4907/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 265.0724\n",
      "Epoch 4908/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 264.8656\n",
      "Epoch 4909/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 264.6591\n",
      "Epoch 4910/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 264.4528\n",
      "Epoch 4911/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 264.2467\n",
      "Epoch 4912/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 264.0409\n",
      "Epoch 4913/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 263.8353\n",
      "Epoch 4914/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 263.6298\n",
      "Epoch 4915/5000\n",
      "1/1 [==============================] - 0s 996us/step - loss: 263.4246\n",
      "Epoch 4916/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 263.2196\n",
      "Epoch 4917/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 263.0148\n",
      "Epoch 4918/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 262.8102\n",
      "Epoch 4919/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 262.6059\n",
      "Epoch 4920/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 262.4017\n",
      "Epoch 4921/5000\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 262.1977\n",
      "Epoch 4922/5000\n",
      "1/1 [==============================] - 0s 765us/step - loss: 261.9940\n",
      "Epoch 4923/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 261.7904\n",
      "Epoch 4924/5000\n",
      "1/1 [==============================] - 0s 998us/step - loss: 261.5872\n",
      "Epoch 4925/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 261.3841\n",
      "Epoch 4926/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 261.1812\n",
      "Epoch 4927/5000\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 260.9785\n",
      "Epoch 4928/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 260.7760\n",
      "Epoch 4929/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 260.5737\n",
      "Epoch 4930/5000\n",
      "1/1 [==============================] - 0s 998us/step - loss: 260.3717\n",
      "Epoch 4931/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 260.1698\n",
      "Epoch 4932/5000\n",
      "1/1 [==============================] - 0s 959us/step - loss: 259.9681\n",
      "Epoch 4933/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 259.7667\n",
      "Epoch 4934/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 259.5654\n",
      "Epoch 4935/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 259.3643\n",
      "Epoch 4936/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 259.1636\n",
      "Epoch 4937/5000\n",
      "1/1 [==============================] - 0s 875us/step - loss: 258.9629\n",
      "Epoch 4938/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 258.7626\n",
      "Epoch 4939/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 258.5624\n",
      "Epoch 4940/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 258.3624\n",
      "Epoch 4941/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 258.1626\n",
      "Epoch 4942/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 257.9631\n",
      "Epoch 4943/5000\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 257.7636\n",
      "Epoch 4944/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 257.5645\n",
      "Epoch 4945/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 257.3656\n",
      "Epoch 4946/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 257.1668\n",
      "Epoch 4947/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 256.9683\n",
      "Epoch 4948/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 256.7700\n",
      "Epoch 4949/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 256.5718\n",
      "Epoch 4950/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 256.3740\n",
      "Epoch 4951/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 256.1763\n",
      "Epoch 4952/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 255.9788\n",
      "Epoch 4953/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 255.7815\n",
      "Epoch 4954/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 255.5845\n",
      "Epoch 4955/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 255.3875\n",
      "Epoch 4956/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 255.1909\n",
      "Epoch 4957/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 254.9944\n",
      "Epoch 4958/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 254.7982\n",
      "Epoch 4959/5000\n",
      "1/1 [==============================] - 0s 998us/step - loss: 254.6021\n",
      "Epoch 4960/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 254.4063\n",
      "Epoch 4961/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 254.2107\n",
      "Epoch 4962/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 254.0153\n",
      "Epoch 4963/5000\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 253.8200\n",
      "Epoch 4964/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 253.6250\n",
      "Epoch 4965/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 253.4302\n",
      "Epoch 4966/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 253.2356\n",
      "Epoch 4967/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 253.0412\n",
      "Epoch 4968/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 252.8469\n",
      "Epoch 4969/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 252.6530\n",
      "Epoch 4970/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 252.4592\n",
      "Epoch 4971/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 252.2656\n",
      "Epoch 4972/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 252.0722\n",
      "Epoch 4973/5000\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 251.8791\n",
      "Epoch 4974/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 251.6861\n",
      "Epoch 4975/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 251.4933\n",
      "Epoch 4976/5000\n",
      "1/1 [==============================] - 0s 980us/step - loss: 251.3008\n",
      "Epoch 4977/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 251.1084\n",
      "Epoch 4978/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 250.9163\n",
      "Epoch 4979/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 250.7243\n",
      "Epoch 4980/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 250.5325\n",
      "Epoch 4981/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 250.3410\n",
      "Epoch 4982/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 250.1497\n",
      "Epoch 4983/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 249.9585\n",
      "Epoch 4984/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 249.7676\n",
      "Epoch 4985/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 249.5769\n",
      "Epoch 4986/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 249.3863\n",
      "Epoch 4987/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 249.1960\n",
      "Epoch 4988/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 249.0059\n",
      "Epoch 4989/5000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 248.8159\n",
      "Epoch 4990/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 248.6263\n",
      "Epoch 4991/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 248.4367\n",
      "Epoch 4992/5000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 248.2474\n",
      "Epoch 4993/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 248.0583\n",
      "Epoch 4994/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 247.8693\n",
      "Epoch 4995/5000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 247.6806\n",
      "Epoch 4996/5000\n",
      "1/1 [==============================] - 0s 999us/step - loss: 247.4922\n",
      "Epoch 4997/5000\n",
      "1/1 [==============================] - 0s 998us/step - loss: 247.3038\n",
      "Epoch 4998/5000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 247.1157\n",
      "Epoch 4999/5000\n",
      "1/1 [==============================] - 0s 345us/step - loss: 246.9278\n",
      "Epoch 5000/5000\n",
      "1/1 [==============================] - 0s 997us/step - loss: 246.7401\n"
     ]
    }
   ],
   "source": [
    "# 训练次数5000\n",
    "history = model.fit(x, y, epochs=5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cb78ae66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Keras is training/fitting/evaluating on array-like data. Keras may not be optimized for this format, so if your input data format is supported by TensorFlow I/O (https://github.com/tensorflow/io) we recommend using that to load a Dataset instead.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[50.017044]], dtype=float32)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 预测\n",
    "model.predict(pd.Series([20]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24e457cd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
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